As increasingly boost the volume of vehicles increases and the travel demand of citizens, the traffic in the urban area has becoming more and more complex. Traffic flow prediction has been an important problem to urban planners, which can help them estimate the changes of the traffic so that they can respond in time to possible traffic jams or congestion [1]}. To get a more accurate prediction, many approaches have been explored [2]}, [3]}, [4]}. With its strong forecasting performance, deep learning has proved sound in traffic flow prediction and various complex models have been proposed to make traffic flow prediction more accurate.
